Fuzzy Control for Semi-Active Vehicle Suspension

Kurczyk, Sebastian; Pawełczyk, Marek

doi:10.1260/0263-0923.32.3.217

Cited by 45 publications

(24 citation statements)

References 14 publications

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“…The double-layer semi-active vibration isolation system of marine auxiliary machinery. From equation (6), two equations can be obtained as follows…”

Section: Model Of Vibration Isolation Systemsmentioning

confidence: 99%

Non-linear dynamic analysis of double-layer semi-active vibration isolation systems using revised Bingham model

Xia

Wang

Hou

et al. 2016

Journal of Low Frequency Noise, Vibration and Active Control

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In this work, a revised Bingham model for magneto-rheological damper is used to investigate the primary resonance reduction in the double-layer semi-active isolation system of marine auxiliary machinery. An analytical solution for the auxiliary double-layer semi-active isolation system's primary resonance is obtained with an averaging method, and this is verified numerically using the Maple software. The effect of model parameters of magneto-rheological damper on the system's vibration transmissibility is studied. The research results show that the damping of magneto-rheological damper and the control force have a significant effect on the vibration transmissibility in the resonance region. The vibration transmissibility of the double-layer semi-active isolation system decreases with increase in damping of the magnetorheological damper and control force. Yet the zero force velocity contributes very little to changes in vibration transmissibility.

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“…The double-layer semi-active vibration isolation system of marine auxiliary machinery. From equation (6), two equations can be obtained as follows…”

Section: Model Of Vibration Isolation Systemsmentioning

confidence: 99%

Non-linear dynamic analysis of double-layer semi-active vibration isolation systems using revised Bingham model

Xia

Wang

Hou

et al. 2016

Journal of Low Frequency Noise, Vibration and Active Control

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“…The front and rear road-induced excitation signals are delayed with each other according to (7), and a preview signal v pv described by (6) is used as the reference signal in FxLMS. The MR dampers are modeled by (8), (9) and (10), together with an additional dynamics described by the filter S Fmr .…”

Section: B Modified Fxlms Intended For Mr Dampersmentioning

confidence: 99%

FxLMS algorithm with preview for vibration control of a half-car model with magnetorheological dampers

Krauze

Kasprzyk

2014

2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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The paper presents a novel approach to the vehicle vibration control using magnetorheological (MR) dampers. Simulation experiments were carried out based on a half-car suspension model including the Bouc-Wen model of the MR damper. It is also assumed that information about the road excitation is available in advance as a preview signal. The tanhbased model of the MR damper was identified according to the Bouc-Wen model response and used to obtain the inverse model that is necessary in the control scheme. The adaptive feedforward LMS (Least Mean Squares) algorithm with a filtered preview signal was modified in order to control the semi-active elements. Because the MR damper can only dissipate energy, it was proposed to decompose the velocity-force characteristics of the damper into a nonlinear passive damper curve and a symmetrical control range of a pseudo active suspension actuator. Such assumption assures that the algorithm can converge to appropriate parameters of the adaptive filter. Deviation of an error signal assumed as kinematic energy of the vehicle body heave vibrations is minimized by the algorithm. Control force generated by the MR dampers and expected by the algorithm is achieved indirectly by the inverse model of the damper. The suspension model was subjected to the roadinduced stimulation in the form of series of bumps within the frequency range 0.5 -15 Hz. Simulation results obtained for the FxLMS (Filtered-x LMS) and Skyhook algorithms demonstrate an advantage of the modified FxLMS due to its ability to adapt to changing conditions.

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“…5 Although the performance of the semi-active suspension is slightly inferior to that of the active suspension, simple structure, low cost and low energy consumption are its main advantages, which make it have good prospects for engineering applications. 6 To improve the control performance of the semi-active suspension, a variety of control methods, such as the optimal control, the sliding mode control and the fuzzy adaptive control, [7][8][9] have been adopted for the controller design. However, these control methods are often difficult to deal with the constraints of system states and damping force regulation when applied to the vehicle semi-active suspension.…”

Section: Introductionmentioning

confidence: 99%

Application of explicit model predictive control to a vehicle semi-active suspension system

Zhang

Yuan

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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The vehicle semi-active suspension is a typical multiple-input multiple-output system with strong couplings, actuator constraints and fast dynamics. This paper addresses the damping force regulation of shock-absorber in vehicle semiactive suspensions using an explicit model predictive control (EMPC) approach, which allows minimizing the system control objective function while satisfying the actuator constraints. The main advantage of the proposed approach is that the control law computation requirement is low, and thus the EMPC system is suitable for implementation in a standard automotive microcontroller. The design of the EMPC system consists of mathematical modeling, objective function determination, controller formulation and simulation validation. Presented simulation results verify that a superior control performance of the vehicle semi-active suspension system is achieved by the proposed EMPC control approach compared with the performance obtained using conventional control method.

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Fuzzy Control for Semi-Active Vehicle Suspension

Cited by 45 publications

References 14 publications

Non-linear dynamic analysis of double-layer semi-active vibration isolation systems using revised Bingham model

Non-linear dynamic analysis of double-layer semi-active vibration isolation systems using revised Bingham model

FxLMS algorithm with preview for vibration control of a half-car model with magnetorheological dampers

Application of explicit model predictive control to a vehicle semi-active suspension system

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